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Optimizing wear of Al 6062 alloy using machine learning and tungsten carbide nanoparticles

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Soundararajan S, E Srimathi, Sudheer Kumar. Battula, R Malkiya Rasalin Prince, N Suresh, Neelam Rawat, V Jaiganesh, V Sharun
» doi: 10.31838/ecb/2023.12.si6.246


This research investigates the wear behavior of Al 6062 alloy reinforced with tungsten carbide nanoparticles (WC) using the stir casting technique. The aim is to optimize the wear properties by varying the composition of WC and analyzing the effects of load, rotational speed, and sliding distance on the friction coefficient (Frc) and wear rate (Wer). The experimental analysis is conducted using the pin-on-disc machine, and the Taguchi L27 array is employed to systematically vary the input parameters. The Taguchi signal-to-noise ratio (SNR) analysis is then performed to identify the optimal combination for minimizing Frc and Wer. The results reveal that the addition of WC nanoparticles improves the wear resistance of the alloy, with the composition of 6% WC exhibiting the most favorable wear characteristics. The linear regression analysis is employed to develop a mathematical equation for predicting the responses. Furthermore, Artificial Neural Networks (ANN) are applied to predict Frc and Wer based on the input parameters, achieving high accuracy in training, validation, and testing phases. The accuracy of the ANN model is found to be 99.89% in training, 99.87% in validation, and 99.87% in testing, indicating its effectiveness in capturing the complex relationships and accurately predicting wear properties. The findings from this research provide valuable insights for material engineers and researchers in the field of wear analysis and nanocomposite development. The optimized Al 6062-WC nanocomposites can be applied in various industries where wear resistance is crucial, such as automotive, aerospace, and manufacturing. The developed mathematical equation and ANN model offer practical tools for predicting and optimizing wear properties, facilitating the design and manufacturing of wear-resistant materials with improved performance and durability. This research contributes to a deeper understanding of the wear behavior of Al 6062-WC nanocomposites and paves the way for further investigations in optimizing their composition and enhancing wear resistance.

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